In recent years, there has been an enormous increase in the amount of digital media available online. Services, such as Apple's iTunes® for example, enable users to legally purchase and download music. Other services, such as Yahoo!® Music Unlimited and RealNetwork's Rhapsody® for example, provide access to millions of songs for a monthly subscription fee. YouTube® provides users access to video media. As a result, media items have become much more accessible to consumers worldwide. However, the increased accessibility of media has only heightened a long-standing problem for the media industry, which is namely the issue of linking users with media that matches their preferences.
Many companies, technologies, and approaches have emerged to address this issue of media recommendation. Media item recommendations may be provided to users as suggestions based on information about the user and/or their media likes or dislikes, also called preferences. Media recommendations may be provided either by service provider companies or by the user's online “friends” (typically identified by user id). In the case of company provided recommendations, some companies assign ratings to attributes of an identified media that are assembled to create a holistic classification for the media that is then used by a recommendation engine. Other companies take a communal approach wherein recommendations are based on the collective wisdom of a group of users with similar tastes by profiling the habits of a particular user and then searching similar profiles of other users.
When a user receives media recommendations, an associated play or popularity rating may follow. The rating is designed to be indicative of the relative play or usage of the media item among users. This allows the user to organize their media item selections for view and/or usage according to popularity. The popularity rating is typically maintained on a remote server or a super peer device accessible to other users' networked devices. The user can select which of their media is played based on the popularity. The play rating may be updated with the user's own usage of given media items which is reported as an input to the popularity rating algorithm for the media item.
One issue associated with popularity or play ratings is that they are not truly indicative of media item usage as a result of recommendations. The play or usage of a media item may affect its play rating regardless of whether such play or usage was attributable to a recommendation. Thus as an example, a single user playing a given media item one thousand (1000) times may increase the play rating of the media in the same manner as one hundred (100) different users each playing the given media item ten (10) times each. Thus, the play rating can be skewed and even manipulated to artificially increase its popularity by a single or small group of users. One hundred (100) different users playing a media item as a result of recommendations may be more useful information to a user in determining which media item to play. However, it cannot be determined whether a media item's play rating was a result of recommendations, and if so, to what extent.